ABSTRACT Accurate brain tumour classification from MRI is essential for computer‐aided diagnosis and treatment planning. This paper proposes TCAFNet, a lightweight deep learning framework that integrates multi‐scale attention mechanisms and transformer‐based refinement to enhance both local feature discrimination and global contextual reasoning. The proposed model is built upon a pre‐trained EfficientNetB0 backbone and employs an early semantic fusion strategy, in which deep features are injected into intermediate layers to guide feature learning from early stages. Cross‐scale dependencies are explicitly modelled using pairwise cross‐attention modules operating on multi‐level feature maps. Each attention‐enhanced representation is further refined using a customized adaptive convolutional block attention module and an adaptive feature pyramid attention block. The refined features are then integrated through an attention‐based fusion mechanism and processed by a lightweight Transformer block to capture long‐range spatial relationships. Experimental results show that the proposed model achieves an accuracy of 99.35% on the test set of the Figshare dataset, and demonstrates strong cross‐dataset robustness with accuracies of 99.29% and 99.67% on the test sets of Nickparvar and Br35H datasets, respectively. These results demonstrate the effectiveness of combining early feature guidance, cross‐scale attention and transformer‐based modelling for robust multi‐class brain tumour classification.
Mohammadi et al. (Thu,) studied this question.